Recent Advances in Computer Science and Communications

Author(s): Jyotsna* and Parma Nand

DOI: 10.2174/2666255816666220819124133

Assessment of Various Scheduling and Load Balancing Algorithms in Integrated Cloud-Fog Environment

Article ID: e190822207727 Pages: 17

  • * (Excluding Mailing and Handling)

Abstract

Cloud computing is a rapidly developing computing technology, which enables to process and store large volumes of information along with providing services to numerous end users across various disciplines. Due to ever-increasing demand with the introduction of technologies like IoT, cloud servers processing capability and storage capacity are rapidly saturating, creating lag in response time. The lag in response time contradicts the never-ending rise of demand for programs associated with real-time analytics and real-time applications. Technologies like Fog computing which works in close association with cloud computing can act as an alternative platform to meet the desired goals. However, due to limited capacity, fog computing-based platforms get exhausted easily. Setting up effective burden allocation algorithms will allow for powerful and well-organized use of both of these platforms. Numerous methods are there to address problems such as task scheduling, resource scheduling, workflow scheduling, load balancing, resource provisioning, and load balancing. The present study compares different aspects of the existing techniques. The current study also explores the quality of service metrics across a variety of existing techniques, providing food for further experimentation.

Background: It is required to design a suitable scheduling algorithm that enhances the timely execution of goals such as load distribution, cost monitoring, minimal time lag to react, increased security awareness, optimized energy usage, dependability, and so on. In order to attain these criteria, a variety of scheduling strategies based on hybrid, heuristic, and meta-heuristic techniques are under consideration.

Objective: IoT devices and a variety of network resources make up the integrated cloud-fog environment. Every fog node has devices that release or request resources. A good scheduling algorithm is required in order to maintain the requests for resources made by various IoT devices.

Methods: This research focuses on the analysis of numerous scheduling challenges and techniques employed in a cloud-fog context. This work evaluates and analyses the most important fog computing scheduling algorithms.

Results: The survey of simulation tools used by the researchers is done. From the compared results, the highest percentage in the literature has 60% of scheduling algorithm which is related to task scheduling and 37% of the researchers have used iFogSim simulation tool for the implementation of the proposed algorithm defined in their research paper.

Conclusion: The findings in the paper provide a roadmap of the proposed efficient scheduling algorithms and can help researchers to develop and choose algorithms close to their case studies.

Keywords: Fog computing, cloud computing, quality-of-service, scheduling, and load balancing

Graphical Abstract

[1]
F. Murtaza, A. Akhunzada, S. Islam, J. Boudjadar, and R. Buyya, "QOS aware service provisioning in fog computing", J. Netw. Comput. Appl., vol. 165, p. 102674, 2020.
[http://dx.doi.org/10.1016/j.jnca.2020.102674]
[2]
F. Bonomi, R. Milito, J. Zhu, and S. Addepalli, "In Fog computing and its role in the internet of things"Proceedings of the first edition of the MCC workshop on Mobile cloud computing (MCC ’12), 2012, pp. 13-16.
[http://dx.doi.org/10.1145/2342509.2342513]
[3]
M. Usman Sana, and Z. Li, "Efficiency aware scheduling techniques in cloud computing: A descriptive literature review", PeerJ Comput. Sci., vol. 7, p. e509, 2021.
[http://dx.doi.org/10.7717/peerj-cs.509] [PMID: 34013035]
[4]
K. Matrouk, and K. Alatoun, "Scheduling algorithms in fog computing: A survey", Int. J. Net. Distri. Comput., vol. 9, no. 1, pp. 59-74, 2021.
[http://dx.doi.org/10.2991/ijndc.k.210111.001]
[5]
T. Francis, "A comparison of cloud execution mechanisms fog, edge, and clone cloud computing", Int. J. Electr. Comput. Eng., vol. 8, no. 6, pp. 4646-4653, 2018.
[http://dx.doi.org/10.11591/ijece.v8i6.pp4646-4653]
[6]
R. Belmahdi, D. Mechta, and S. Harous, "A survey on various methods and algorithms of scheduling in fog computing"Ing.des Syst. d’Information,, vol. 26. 2021, no. 2, pp. 211-224.
[http://dx.doi.org/10.18280/isi.260208]
[7]
R. Mahmud, K. Ramamohanarao, and R. Buyya, "Application management in fog computing environments: A taxonomy, review and future directions", ACM Comput. Surv., vol. 53, no. 4, 2020.
[http://dx.doi.org/10.1145/3403955]
[8]
S.S. Gill, R. Arya, G. Wander, and R. Buyya, "Fog based smart healthcare as a big data and cloud service for heart patients using IoT"International Conference on Intelligent Data Communication Technologies and Internet of Things (ICICI), 2018, pp. 1376-1383.
[http://dx.doi.org/10.1007/978-3-030-03146-6_161]
[9]
F.H. Atlam, R.J. Walters, and G.B. Wills, "Fog computing and the internet of things: A review", Big Data Cogn. Comput., vol. 2, no. 2, p. 10, 2018.
[http://dx.doi.org/10.3390/bdcc2020010]
[10]
G. Rahman, and C.C. Wen, "Fog computing, applications, security and challenges", IACSIT Int. J. Eng. Technol., vol. 7, no. 3, pp. 1615-1621, 2018.
[http://dx.doi.org/10.14419/ijet.v7i3.12612]
[11]
D. Rahbari, and M. Nickray, "Computation offloading and scheduling in edge fog cloud computing", J. Electron. Inf. Syst., vol. 1, no. 1, pp. 26-36, 2019.
[http://dx.doi.org/10.30564/jeisr.v1i1.1135]
[12]
C. Barros, V. Rocio, A. Sousa, and H. Paredes, "Scheduling in cloud and fog architecture: Identification of limitations and suggestion of improvement perspectives", J. Inf. Syst. Eng. Manag., vol. 5, no. 3, p. em0121, 2020.
[http://dx.doi.org/10.29333/jisem/8429]
[13]
M. Firdhous, O. Ghazali, and S. Hassan, "Fog computing: Will it be the future of cloud computing?" The Third International Conference on Informatics & Applications (ICIA2014), Kuala Terengganu, Malaysia, 2014.
[14]
S.R. Hassan, I. Ahmad, S. Ahmad, A. Alfaify, and M. Shafiq, "Remote pain monitoring using fog computing for e-Healthcare: An efficient architecture", Sensors , vol. 20, no. 22, p. 6574, 2018.
[http://dx.doi.org/10.3390/s20226574]
[15]
M.M. Mon, and M.A. Khine, "Scheduling and load balancing in cloud-fog computing using swarm optimization techniques : A survey". Available from:http://onlineresource.ucsy.edu.mm/bitstream/handle/123456789/1119/ICCA
[16]
J. Bisht, and V. Subrahmanyam, "Survey on load balancing and scheduling algorithms in cloud integrated fog environment"2nd International Conference on ICT for Digital, Smart and Sustainable Development (ICIDSSD'20),27 Feb, 2020,, Jamia Hamdard, New Delhi,, 2020, p. 220, .
[http://dx.doi.org/10.4108/eai.27-2-2020.2303123]
[17]
S. Anwar, A. Ajmal, F. Hayder, and S. Bibi, "Evaluating cloud & fog computing based on shifting & scheduling algorithms, latency issues and service architecture", Int. J. Comput. Sci. Inf. Secur., vol. 16, no. 6, pp. 9-14, 2018.
[18]
S.Z. Kottur, J. Geetha, D.S. Jayalaksmi, T. Surabhi, R.R. Ganiga, and M. Gupta, "A study on scheduling in fog computing", Int. J. Adv. Sci. Technol, vol. 29, no. 10, 2020.
[19]
T.N. Gia, M. Jiang, A. Rahmani, T. Westerlund, P. Liljeberg, and H. Tenhunen, "Fog computing in healthcare internet of things: A case study on ECG feature extraction"2015 IEEE Int. Conf. Comput. Inform. Technol.,, 2015, pp. 356-363.
[http://dx.doi.org/10.1109/CIT/IUCC/DASC/PICOM.2015.51]
[20]
M. Kaur, and R. Aron, "A systematic study of load balancing approaches in the fog computing environment", J. Supercomput., vol. 77, no. 8, pp. 9202-9247, 2021.
[http://dx.doi.org/10.1007/s11227-020-03600-8]
[21]
M. Gm, M. Kolhar, and A. Alameen, "Load balancing at fog nodes using scheduling algorithms", Int. J. Recent Technol. Eng., vol. 8, no. 6, pp. 4129-4134, 2020.
[http://dx.doi.org/10.35940/ijrte.F9238.038620]
[22]
K. Amir, H. Abdelhakim, and El. Mostapha , ""On the fog-cloud cooperation: How fog computing can address latency concerns of IoT application",", Fourth International Conference on Fog And Mobile Edge Computing (FMEC), IEEE, 10-13 Jun, 2019,, Rome,: Italy,, pp. 166-172, 2019.
[23]
Y. Tan, C. Yu, S. Zheng, and K. Ding, "Introduction to fireworks algorithm", Int. J. Swarm Intell. Res., vol. 4, no. 4, pp. 39-70, 2015.
[http://dx.doi.org/10.4018/ijsir.2013100103]
[24]
Z. Tang, L. Qi, Z. Cheng, K. Li, S.U. Khan, and K. Li, "An energy efficient task scheduling algorithm in DVFS enabled cloud environment", J. Grid Comput., vol. 14, no. 1, pp. 55-74, 2016.
[http://dx.doi.org/10.1007/s10723-015-9334-y]
[25]
S. Alhat, N. Bangal, A. Gaikwad, and S. Khairnar, "Enhancing data security in IoT healthcare services using fog computing", Int. J. Eng. Res. Technol. , vol. 6, no. 12, pp. 2395-2456, 2019.
[26]
A.M. Alsmadi, "Fog computing scheduling algorithm for smart city", Iran. J. Electr. Comput. Eng., vol. 11, no. 3, pp. 2219-2228, 2021.
[IJECE [http://dx.doi.org/10.11591/ijece.v11i3.pp2219-2228]
[27]
J. Lansky, M. Mohammadi, A.H. Mohammed, S.H.T. Karim, S. Rashidi, and A.M. Rahmani, " Scientific workflow scheduling in mobile edge computing based on a discrete butterfly optimization algorithm", Research gate, 2021.
[http://dx.doi.org/10.21203/rs.3.rs-208986/v1]
[28]
J.C. Guevara, and N.L.S. da Fonseca, "Task scheduling in cloud-fog computing systems", Peer-to-Peer Netw. Appl., vol. 14, no. 2, pp. 962-977, 2021.
[http://dx.doi.org/10.1007/s12083-020-01051-9]
[29]
R. Madhura, B.L. Elizabeth, and V.R. Uthariaraj, An improved listbased task scheduling algorithm for fog computing environment., Springer Vienna, 2021, vol. 103, no 7, pp. 1353-1389, 2021.
[http://dx.doi.org/10.1007/s00607-021-00935-9]
[30]
T.S. Nikoui, A. Balador, A.M. Rahmani, and Z. Bakhshi, "In Cost aware task scheduling in fog cloud environment"2020 CSI/CPSSI International Symposium on Real Time and Embedded Systems and Technologies (RTEST), 2020, pp. 1-8.
[http://dx.doi.org/10.1109/RTEST49666.2020.9140118]
[31]
C. Yin, T. Li, X. Qu, and S. Yuan, "An improved ant colony optimization job scheduling algorithm in fog computing", International Symposium on Artificial Intelligence and Robotics Oct, 2020, vol. 11574. 2020.
[http://dx.doi.org/10.1117/12.2580303]
[32]
M. Barzegaran, V. Karagiannis, C. Avasalcai, P. Pop, S. Schulte, and S. Dustdar, "Towards extensibility aware scheduling of industrial applications on fog nodes"2020 IEEE International Conference on Edge Computing (EDGE), 2020, pp. 67-75.
[http://dx.doi.org/10.1109/EDGE50951.2020.00018]
[33]
A. Madej, N. Wang, N. Athanasopoulos, R. Ranjan, and B. Varghese, ""Priority-based fair scheduling in edge computing", ", In: 2020 IEEE 4th International Conference on Fog and Edge Computing (ICFEC),, 2020.
[http://dx.doi.org/10.1109/ICFEC50348.2020.00012]
[34]
X. Li, L. Zhou, Y. Sun, and B. Ulziinyam, "Multi-task offloading scheme for UAV-enabled fog computing networks", J. Wire. Com. Net., vol. 230, 2020.
[http://dx.doi.org/10.1186/s13638-020-01825-y]
[35]
Z. Shi, and Z. Shi, "Multi node task scheduling algorithm for edge computing based on multi objective optimization", J. Phys. Conf. Ser., vol. 1607, no. 1, 2020.
[http://dx.doi.org/10.1088/1742-6596/1607/1/012017]
[36]
J. Wang, and D. Li, "Task scheduling based on a hybrid heuristic algorithm for smart production line with fog computing", Sensors , vol. 19, no. 5, p. E1023, 2019.
[http://dx.doi.org/10.3390/s19051023] [PMID: 30823391]
[37]
M.A. Basset, D. Shahat, M. Elhoseny, and H. Song, "Energy aware metaheuristic algorithm for industrial internet of things task scheduling problems in fog computing applications", IEEE Internet Things J., vol. 8, no. 16, pp. 12638-12649, 2021.
[http://dx.doi.org/10.1109/JIOT.2020.3012617]
[38]
M. Bhatia, S.K. Sood, and S. Kaur, "Quantumized approach of load scheduling in fog computing environment for IOT applications", Computing, vol. 102, no. 5, pp. 1097-1115, 2020.
[http://dx.doi.org/10.1007/s00607-019-00786-5]
[39]
O.A. Khan, "A cache based approach toward improved scheduling in fog computing", Softw. Pract. Exper., vol. 51, no. 12, pp. 2360-2372, 2021.
[http://dx.doi.org/10.1002/spe.2824]
[40]
P. Gazori, D. Rahbari, and M. Nickray, "Saving time and cost on the scheduling of fog-based IOT applications using deep reinforcement learning approach", Future Gener. Comput. Syst., 2019.
[http://dx.doi.org/10.1016/j.future.2019.09.060]
[41]
B. Jamil, M. Shojafar, I. Ahmed, A. Ullah, K. Munir, and H. Ijaz, "A job scheduling algorithm for delay and performance optimization in fog computing", Concurr. Comput., vol. 32, no. 7, pp. 1-13, 2020.
[http://dx.doi.org/10.1002/cpe.5581]
[42]
R. Vijayalakshmi, V. Vasudevan, S. Kadry, and R. Lakshmana Kumar, "Optimization of makespan and resource utilization in the fog computing environment through task scheduling algorithm", Int. J. Wavelets Multiresolution Inf. Process., vol. 18, no. 1, pp. 1-12, 2020.
[http://dx.doi.org/10.1142/S021969131941025X]
[43]
G. Li, Y. Liu, J. Wu, D. Lin, and S. Zhao, "Methods of resource scheduling based on optimized fuzzy clustering in fog computing", Sensors , vol. 19, no. 9, p. E2122, 2019.
[http://dx.doi.org/10.3390/s19092122] [PMID: 31071923]
[44]
C.G. Wu, and L. Wang, "A deadline aware estimation of distribution algorithm for resource scheduling in fog computing systems"2019 IEEE Congress on Evolutionary Computation (CEC) 10-13 Jun, 2019, , Wellington, New Zealand, 2019, pp. 660-666.
[http://dx.doi.org/10.1109/CEC.2019.8790305]
[45]
H. Rafique, M.A. Shah, S.U. Islam, T. Maqsood, S. Khan, and C. Maple, "A novel bio-inspired hybrid algorithm (NBIHA) for efficient resource management in fog computing", IEEE Access, vol. 7, pp. 115760-115773, 2019.
[http://dx.doi.org/10.1109/ACCESS.2019.2924958]
[46]
E. Ghaffari, "Providing a new scheduling method in fog network using the ant colony algorithm", Collection of Articles on Computer Science, Scipedia., 2019. Available from: [https://www.scipedia.com/public/Ghaffari_2019a
[47]
R. Xu, "Improved particle swarm optimization based workflow scheduling in cloud-fog environment", Lect. Notes Bus. Inf. Process., vol. 342, pp. 337-347, 2019.
[http://dx.doi.org/10.1007/978-3-030-11641-5_27]
[48]
S.F. Hassan, and R.F. Ghani,, "PWRR algorithm for video streaming process using fog computing", Baghdad Sci. J., vol. 16, no. 3, pp. 667-676, 2019.
[http://dx.doi.org/10.21123/bsj.2019.16.3.0667]
[49]
S.P. Singh, A. Nayyar, H. Kaur, and A. Singla, "Dynamic task scheduling using balanced VM allocation policy for fog computing platforms", Scalable Comput. Pract. Exper., vol. 20, no. 2, pp. 433-457, 2019.
[http://dx.doi.org/10.12694/scpe.v20i2.1538]
[50]
H.E. Refaat, and M.A. Mead, "DLBS: Decentralize load-balance scheduling algorithm for real time IoT services in mist computing", Int. J. Adv. Comput. Sci. Appl., vol. 10, no. 9, 2019.
[http://dx.doi.org/10.14569/IJACSA.2019.0100913]
[51]
L. Liu, D. Qi, N. Zhou, and Y. Wu, "A task scheduling algorithm based on classification mining in fog computing environment", Wirel. Commun. Mob. Comput., pp. 1-11, 2018.
[http://dx.doi.org/10.1155/2018/2102348]
[52]
T. Choudhari, M. Moh, and T.S. Moh, "Prioritized task scheduling in fog computing"ACMSE ’18: Proceedings of the ACMSE 2018 Conference March 2018, 2018, pp. 1-8.
[http://dx.doi.org/10.1145/3190645.3190699]
[53]
X.Q. Pham, N.D. Man, N.D.T. Tri, N.Q. Thai, and E.N. Huh, "A cost and performance-effective approach for task scheduling based on collaboration between cloud and fog computing", Int. J. Distrib. Sens. Netw., vol. 13, no. 11, 2017.
[http://dx.doi.org/10.1177/1550147717742073]
[54]
S. Kabirzadeh, D. Rahbari, and M. Nickray, "A hyper heuristic algorithm for scheduling of fog networks"21st Conference of Open Innovations Association (FRUCT) 06-10 Nov, 2017, Helsinki, Finland, 2017, pp. 148-155.
[http://dx.doi.org/10.23919/FRUCT.2017.8250177]
[55]
M. Verma, N. Bhardwaj, and A.K. Yadav, "Real time efficient scheduling algorithm for load balancing in fog computing environment", Int. J. Inform. Technol. Comput. Sci., vol. 8, pp. 1-10, 2016.
[http://dx.doi.org/10.5815/ijitcs.2016.04.01]
[56]
Z. Liu, J. Li, Z. Xu, S. Xu, Q. Lin, J. Qiu, J. Tang, and Y. Wang, "Deep reinforcement learning with double q-learning", Thirtieth AAAI Conference on Artificial Intelligence 02 Mar, 2016, vol. 30, no. 1, pp. 2094-2100, 2016.
[57]
M. Xiao, Y. Yin, Y. Zhou, and S. Pan, "In Research on improvement of apriori algorithm based on marked transaction compression", Proceedings of the 2nd IEEE Advanced Information Technology, Electronic and Automation Control Conference, (IAEAC ’17), 2017,, vol. 748. pp. 1067-1071.
[http://dx.doi.org/10.1109/IAEAC.2017.8054177]
[58]
J. Yang, H. Huang, and X. Jin, "Mining web accesssequence with improved apriori algorithm", Proceedings of the 20thIEEE International Conference on Computational Science and Engineering and 15th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, 22-23 Jul, 2017,, Guangzhou, China,, pp. 780-784.
[59]
M. Kalra, and S. Singh, "A review of metaheuristic scheduling techniques in cloud computing", Egyptian Informat.J., vol. 16, no. 3, pp. 275-295, 2015.
[http://dx.doi.org/10.1016/j.eij.2015.07.001]
[60]
K-C. Lin, Y-H. Huang, J.C. Hung, and Y-T. Lin, "Modified cat swarm optimization algorithm for feature selection of support vector machines", In: Frontier and Innovation in Future Computing and Communications., Springer: Dordrecht, Netherlands, 2014, pp. 329-336.
[61]
B. Kashif, and E. Aiman, "Edge computing for interactive media and video streaming"Second International Conference on Fog and Mobile Edge Computing (FMEC) 08-11 May, 2017, Valencia, Spain, 2017, pp. 68-73.
[62]
A. Ruia, C.J. Casey, S. Saha, and A. Sprintson, Flowcache: A cache-based approach for improving SDN scalability2016 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 10-14 April, 2016,, San Francisco, CA, USA,, 2016, pp. 610-615.
[http://dx.doi.org/10.1109/INFCOMW.2016.7562149]
[63]
L.F. Bittencourt, J. Montes, R. Buyya, O.F. Rana, and M. Parashar, "Mobility-aware application scheduling in fog computing", IEEE Cloud Comput, vol. 4, no. 2, pp. 26-35, 2017.
[http://dx.doi.org/10.1109/MCC.2017.27]
[64]
W. Chen, and E. Deelman, ""Workflowsim: A toolkit for simulating scientific workflows in distributed environments"", In: 2012 IEEE 8th International Conference on E-Science, 08-12 Oct, 2012,, Chicago, IL,: USA,, pp. 1-8, 2012.
Available from:https://github.com/WorkflowSim [http://dx.doi.org/10.1109/eScience.2012.6404430]
[65]
R.N. Calheiros, R. Ranjan, A. Beloglazov, C.A.F. DeRose, and R. Buyya, "CloudSim: A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms", Softw. Pract. Exper., vol. 41, no. 1, pp. 23-50, 2011.
[http://dx.doi.org/10.1002/spe.995]
[66]
M. Mahmud, and R. Buyya, "Modeling and simulation of fog and edge computing environments using iFogSim toolkit", Fog. Edge. Comput. Pri. Paradigms Pri. Paradigms, pp. 433-465, 2019.
[http://dx.doi.org/10.1002/9781119525080.ch17]
[67]
B. Wickremasinghe, R.N. Calheiros, and R. Buyya, "CloudAnalyst: A cloudsim-based visual modeller for analysing cloud computing environments and applications" 2010 24th IEEE International Conference on Advanced Information Networking and Applications, 20-23 April, 2010,, Perth, WA, Australia,, 2010, pp. 446-452.
[http://dx.doi.org/10.1109/AINA.2010.32]